Consistency of Causal Inference under the Additive Noise Model

Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):478-486, 2014.

Abstract

We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. We derive general conditions under which the given family of inference methods consistently infers the causal direction in a nonparametric setting.

Related Material

@InProceedings{pmlr-v32-kpotufe14,
title = {Consistency of Causal Inference under the Additive Noise Model},
author = {Samory Kpotufe and Eleni Sgouritsa and Dominik Janzing and Bernhard Schoelkopf},
booktitle = {Proceedings of the 31st International Conference on Machine Learning},
pages = {478--486},
year = {2014},
editor = {Eric P. Xing and Tony Jebara},
volume = {32},
number = {2},
series = {Proceedings of Machine Learning Research},
address = {Bejing, China},
month = {22--24 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v32/kpotufe14.pdf},
url = {http://proceedings.mlr.press/v32/kpotufe14.html},
abstract = {We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. We derive general conditions under which the given family of inference methods consistently infers the causal direction in a nonparametric setting.}
}

%0 Conference Paper
%T Consistency of Causal Inference under the Additive Noise Model
%A Samory Kpotufe
%A Eleni Sgouritsa
%A Dominik Janzing
%A Bernhard Schoelkopf
%B Proceedings of the 31st International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2014
%E Eric P. Xing
%E Tony Jebara
%F pmlr-v32-kpotufe14
%I PMLR
%J Proceedings of Machine Learning Research
%P 478--486
%U http://proceedings.mlr.press
%V 32
%N 2
%W PMLR
%X We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. We derive general conditions under which the given family of inference methods consistently infers the causal direction in a nonparametric setting.